We compared Vec2Instance with Mask RCNN [1]}, which is a current state-of-the-art method in instance segmentation, as well as U-Net [2]}, which is a current state-of-the-art method for semantic segmentation. For Mask R-CNN, we used the MatterPort Mask R-CNN implementation [3]} based on Python 3, Keras, and TensorFlow. ResNet-50 [4]} was used as a backbone, and parameters were initialized to pre-trained values from the COCO dataset [5]}. Only stage 3 and the upper stages of ResNet-50 were fine-tuned, keeping other parameters fixed. In terms of the number of parameters, the Mask R-CNN is very large compared to Vec2Instance. We designed the U-Net network to have 421,793 parameters, nearly the same number of parameters as in Vec2Instance (166,305 parameters in the centroid estimation CNN and 182,945 parameters in the instance segmentation CNN).
